Article
Biochemical Research Methods
Ruoyu Tang, Xinyu He, Ruiqi Wang
Summary: The study presents a general computational method for constructing maps between different cell fates and parametric conditions by systematic perturbations. The method does not require accurate parameter measurements or bifurcations. The maps obtained can help in understanding how systematic perturbations drive cell fate decisions and transitions, providing valuable information for predicting and controlling cell states.
Article
Automation & Control Systems
Min Ding, Hao Zhou, Hua Xie, Min Wu, Kang-Zhi Liu, Yosuke Nakanishi, Ryuichi Yokoyama
Summary: The paper introduces a time series model based on hybrid-kernel least-squares support vector machine for short-term wind power prediction, involving processes of decomposition, classification, and reconstruction. By decomposing and classifying wind power time series, establishing support vector machine models with three different kernels, optimizing parameters, and reconstructing outputs, the model outperforms benchmark models in predicting wind power.
Article
Neurosciences
Amir Soleymankhani, Vahid Shalchyan
Summary: This paper proposes a spike sorting method based on optimized wavelet parameter selection and validates it on simulated and publicly available datasets. The results demonstrate the superior performance of the spike sorting algorithm with optimized wavelet parameters in decoding real intracortical data.
Article
Economics
Chufu Wen, Haoyang Zhu, Zhifeng Dai
Summary: This paper introduces a new aligned technical index that uses the partial least squares (PLS) method to eliminate common noise components in technical indicators and explores the predictability of commodity price returns. The results show that the novel aligned technical index has strong statistical and economic predictive ability in both in-sample and out-of-sample conditions. It outperforms macroeconomic variables and is consistent and stable in various robustness tests.
Article
Environmental Sciences
Keruo Guo, Xuejian Li, Huaqiang Du, Fangjie Mao, Chi Ni, Qi Chen, Yanxin Xu, Zihao Huang
Summary: This study successfully developed a model for analyzing the V-cmax(25) of moso bamboo leaves using hyperspectral remote sensing, combining wavelet decomposition and partial least squares regression. The WVI showed higher correlation and better prediction accuracy compared to the HVI, making it an effective indicator for the inversion of V-cmax(25) in moso bamboo forests.
Article
Chemistry, Analytical
Haoran Li, Suyi Chen, Jisheng Dai, Xiaobo Zou, Tao Chen, Tianhong Pan, Melvin Holmes
Summary: The proposed method introduces a new fast burst-sparsity learning approach for baseline correction, utilizing downsampling strategy and pattern-coupled prior to overcome the limitations of existing baseline correction methods. The study demonstrates that burst-sparsity commonly occurs in peak zones of spectra and can be properly utilized to enhance baseline correction performance.
ANALYTICAL CHEMISTRY
(2022)
Article
Automation & Control Systems
Abbas Saadatmandi, Mahmoud Reza Sohrabi, Hasan Kabiri Fard
Summary: In this study, a fast, easy, inexpensive, and precise method combining UV-Vis spectrophotometry, continuous wavelet transform, and partial least squares multivariate calibration was developed for the simultaneous determination of paracetamol, diphenhydramine, and phenylephrine in tablet dosage form without extraction. The method was validated using synthetic mixtures and showed good performance.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Engineering, Civil
Mostafa Rezaali, John Quilty, Abdolreza Karimi
Summary: The study aims to forecast short-term urban water demand using the Wavelet Data-Driven Forecasting Framework and examines the effectiveness of different machine learning models, dataset partitioning methods, and input variable selection approaches. Real-world case studies show that probabilistic RF and its 'best' wavelet-based version provide the most accurate and reliable forecasts, while permutation- and bootstrap-based dataset partitioning approaches have potential to reduce overfitting. Wavelet decomposition improves model performance and RFIVS significantly reduces the number of input variables used in the ML models while improving performance.
JOURNAL OF HYDROLOGY
(2021)
Article
Automation & Control Systems
Zhonghao Xie, Xi'an Feng, Xiaojing Chen
Summary: This paper proposes a robust method for PLS based on the idea of least trimmed squares (LTS), which effectively deals with high-dimensional regressors. By formulating the LTS problem as a concave maximization problem, the complexity of solving LTS is simplified. The results from simulation and real data sets demonstrate the effectiveness and robustness of the proposed approach.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Environmental Sciences
Zijin Bian, Lina Sun, Kang Tian, Benle Liu, Biao Huang, Longhua Wu
Summary: This study utilized hyperspectral technology combined with partial least squares regression (PLSR) and extreme learning machine (ELM) to establish estimation models for predicting the contents of toxic metals in multi-media environments. The study also explored the best combinations of spectral preprocessing and machine learning algorithms to obtain models with high accuracy. The results showed that ELM-based spectral estimation models can predict metal concentrations with high accuracy and efficiency, providing a potential new approach for estimating toxic metal contamination.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Chemistry, Applied
Barbara Zani Agnoletti, Gabriely Silveira Folli, Lucas Louzada Pereira, Patricia Fontes Pinheiro, Rogerio Carvalho Guarconi, Emanuele Catarina da Silva Oliveira, Paulo Roberto Filgueiras
Summary: The research proposes the combination of instrumental methods with multivariate calibration to predict the quality of arabica coffee in support of sensory analysis. By analyzing volatile compounds, 15 substances were identified as predictors of coffee quality, with 1-nonadecene being highlighted as a new significant compound for sensory attributes prediction.
Article
Plant Sciences
Jingang Wang, Tian Tian, Haijiang Wang, Jing Cui, Xiaoyan Shi, Jianghui Song, Tiansheng Li, Weidi Li, Mingtao Zhong, Wenxu Zhang
Summary: Soil salinization poses a challenge to crop production in arid areas, but spectral analysis and remote sensing technology can assist in assessing the impact of salinity stress on crop photosynthesis.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Multidisciplinary Sciences
Emilio Gomez-Gonzalez, Alejandro Barriga-Rivera, Beatriz Fernandez-Munoz, Jose Manuel Navas-Garcia, Isabel Fernandez-Lizaranzu, Francisco Javier Munoz-Gonzalez, Ruben Parrilla-Giraldez, Desiree Requena-Lancharro, Pedro Gil-Gamboa, Cristina Rosell-Valle, Carmen Gomez-Gonzalez, Maria Jose Mayorga-Buiza, Maria Martin-Lopez, Olga Munoz, Juan Carlos Gomez-Martin, Maria Isabel Relimpio-Lopez, Jesus Aceituno-Castro, Manuel A. Perales-Esteve, Antonio Puppo-Moreno, Francisco Jose Garcia-Cozar, Lucia Olvera-Collantes, Raquel Gomez-Diaz, Silvia de los Santos-Trigo, Monserrat Huguet-Carrasco, Manuel Rey, Emilia Gomez, Rosario Sanchez-Pernaute, Javier Padillo-Ruiz, Javier Marquez-Rivas
Summary: This study demonstrates the feasibility of using hyperspectral image analysis in the visible and near-infrared range for primary screening of SARS-CoV-2. By applying spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence, information can be extracted from fluid samples and analyzed quantitatively and descriptively. The proposed technology is reagent-free, fast, scalable, and could significantly reduce the number of molecular tests required for COVID-19 mass screening, even in resource-limited settings.
SCIENTIFIC REPORTS
(2022)
Article
Construction & Building Technology
Ruiyang Jian, Xiaodi Hu, Tao Han, Jiuming Wan, Wenxia Gan, Zongwu Chen, Yinglong Zhang, Chongfu Cao
Summary: This study investigated how induction heating parameters determine the self-healing capacity of asphalt mixture. The multi-parameter method showed better results in improving the healing ratio compared to the single-parameter method, and the coupling effect among parameters was confirmed.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Multidisciplinary Sciences
Freeh N. Alenezi
Summary: The study introduces a method for variable selection in high dimensional data modeling, using majority scoring with backward elimination in PLS to improve prediction accuracy. The method performs well in predicting corn and diesel contents, while also examining the impact of data properties on prediction behavior.
SCIENTIFIC REPORTS
(2021)
Article
Green & Sustainable Science & Technology
Zhaoxia Wang, Jing Zhao
Article
Energy & Fuels
Jing Zhao, Yaoqi Duan, Xiaojuan Liu
Article
Energy & Fuels
Jing Zhao, Yu Shan
Article
Energy & Fuels
Jing Zhao, Yahui Du
Article
Energy & Fuels
Jing Zhao, Yu Shan
Article
Thermodynamics
Jing Zhao, Yaoqi Duan, Xiaojuan Liu
Article
Construction & Building Technology
Zhao Jing, Li Jiayu
BUILDING AND ENVIRONMENT
(2020)
Article
Energy & Fuels
Jing Zhao, Yahui Du
Article
Construction & Building Technology
Jing Zhao, Jiayu Li, Yu Shan
Summary: This paper proposes a load- and time delay-based model predictive control district energy system model, validates its superiority through experiments, and demonstrates the reduction in energy consumption.
ENERGY AND BUILDINGS
(2021)
Article
Chemistry, Multidisciplinary
Jing Zhao, Dehan Liu, Shilei Lu
Summary: The application of attached sunspace passive solar heating systems (ASPSHS) can improve building performance, reduce energy consumption, and carbon emissions. This paper presents a zero-state response control strategy for the opening and closing time of active interior window in the ASPSHS, to better utilize the attached sunspace for heat transfer control and natural ventilation. Experimental results show that the strategy effectively increases indoor temperature and achieves significant energy savings and emission reductions.
APPLIED SCIENCES-BASEL
(2022)
Article
Green & Sustainable Science & Technology
Yahui Du, Zhihua Zhou, Jing Zhao
Summary: With the increasing refinement of building functions and regions, researchers have proposed a regulation model that dynamically adjusts the set point temperature in different areas to reduce energy load and improve energy efficiency. Experimental results show that this strategy can reduce load demand by about 6.16% without sacrificing indoor comfort. Further optimization and control strategies can achieve overall energy savings of 12.78% for HVAC systems.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Construction & Building Technology
Jing Zhao, Haonan Li, Dehan Liu
Summary: This paper investigates the Attached Sunspace with Breathing Window (ASBW) as a solution to the problems of uneven indoor temperature distribution and poor thermal comfort in traditional attached sunspace heating. The results suggest that the Bottom outlet scheme and the Top inlet scheme provide better local thermal comfort.
BUILDING AND ENVIRONMENT
(2022)
Article
Construction & Building Technology
Jing Zhao, Linyu Shi, Jiayu Li, Haonan Li, Qi Han
Summary: This study quantified the time-delay characteristics of radiant air conditioning systems and established short-term load forecasting and system regulation models based on predictive modeling. The results showed that the system controlled by model predictive control had higher stability in indoor temperature and lower energy consumption.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Jing Zhao, Xiulian Yuan, Yaoqi Duan, Haonan Li, Dehan Liu
Summary: This paper proposes an artificial intelligence-driven method for forecasting building loads by integrating the thermal load characteristics of the building. By establishing independent models for the building envelope and occupant behavior, and coupling the predicted values of these models, the total load forecasting model is created. The results of a case study show that the proposed method achieves excellent predictive performance and can effectively forecast the formation mechanism of thermal loads.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)